Scaling Transition Intelligence

This briefing by RMI describes how financial institutions can use forward-looking, asset-level data to conduct corporate transition assessments. Using a case study of the steelmaker ArcelorMittal and data from Forward Analytics, the document demonstrates how analyzing investment pipelines against company targets and global or regional transition pathways can identify 'execution gaps,' transition risks, and financing opportunities across sustainability, risk, and front-office functions.

• Financial institutions can use bottom-up, asset-level data to estimate the future emissions intensity trajectories of companies, allowing for consistent, like-for-like comparisons regardless of the quality of corporate disclosures.
• Comparing a company's projected emissions trajectory based on its current investment pipeline to its stated targets can reveal 'execution gaps.' In the case of ArcelorMittal, the company is projected to fall short of its 2030 emissions intensity targets for both its group-level and European operations based on public investment plans.
• Alignment assessments against global transition pathways, such as those from the IEA, help financial institutions understand a company's exposure to transition risks. ArcelorMittal's projected trajectory is misaligned with the IEA Net Zero Emissions by 2050 (NZE), Announced Pledges Scenario (APS), and Stated Policy Scenario (STEPS).
• Asset-level data allows for the use of region-specific transition pathways, which provides more granular intelligence on local dependencies and risk drivers. ArcelorMittal was found to be misaligned with the Mission Possible Partnership's (MPP) Europe-specific 'Carbon cost' and 'Technology moratorium' scenarios.
• Analyzing the underlying technology mix of a company's investment pipeline can identify risks of carbon lock-in. While ArcelorMittal is increasing its use of lower-carbon direct reduced iron–electric arc furnace (DRI-EAF) technology, its plans to expand carbon-intensive blast furnace–basic oxygen furnace (BF-BOF) capacity may lead to lock-in due to the ~20-year economic life span of those assets.
• Transition intelligence derived from asset-level data serves three primary functions within financial institutions: sustainability teams can assess portfolio-level climate targets; risk teams can perform ex ante risk assessments and set region-specific thresholds; and front-office teams can identify specific financing solutions for lower-carbon technology build-out.

APA

RMI (2026). Scaling Transition Intelligence. https://rmi.org/resources/scaling-transition-intelligence/

Chicago

RMI. Scaling Transition Intelligence. 2026. https://rmi.org/resources/scaling-transition-intelligence/.

Wikipedia

{{cite report |author=RMI |title=Scaling Transition Intelligence |date=13 April 2026 |url=https://rmi.org/resources/scaling-transition-intelligence/ |access-date=17 August 2026 |via=Climate Insights Directory}}

BibTeX

@techreport{rmi2026scaling, author = {{RMI}}, title = {{Scaling Transition Intelligence}}, institution = {RMI}, year = {2026}, month = apr, url = {https://rmi.org/resources/scaling-transition-intelligence/}, urldate = {2026-08-17}, note = {Indexed by Climate Insights Directory} }